The Residential History File: Studying
Nursing Home Residents’ Long-Term
Orna Intrator,JeffreyHiris, KatherineBerg, Susan C.Miller,and
Objective. To construct a data tool, the Residential History File (RHF), that summa-
rizes information from Medicare claims and nursing home (NH) Minimum Data Set
(MDS) assessments to track people through health care locations, including non-Med-
icare-paid NH stays.
Data Sources. Online Survey of Certification and Reporting (OSCAR) data for 202
free-standing NHs, Medicare Denominator, claims (parts A and B), and MDS assess-
ments for 60,984 people who were present in one of these NHs in 2006.
Methods. The algorithm creating the RHF is outlined and the RHF for the study data
are used to describe place of death. The identification of residents in NHs is compared
with the reports in OSCAR and part B claims.
Principal Findings. The RHF correctly identified 84.8 percent of part B claims with
place-of-service in NH, and it identified 18.3 less residents on average than reported in
the OSCAR on the day of the survey. The RHF indicated that 17.5 percent non-
Medicare NH decedents were transferred to the hospital to die versus 45.6 percent
skilled nursing facility decedents.
Conclusions. The population-based design of the RHF makes it possible to conduct
policy-relevant research to examine the variation in the rate and type of health care
transitions across the United States.
Key Words. Medicare, Minimum Data Set (MDS), transitions in care settings,
linking administrative files, tracking health care utilization
The adoption of prospective payment, first for hospitals in the early 1980s and
then postacute settings a decade ago, created conflicting payment silos, with
hospitals reducing length of stay and postacute providers accepting complex
nThe views expressed in this article are those of the authors and do not necessarily reflect the
position or policy of the Department of Veterans Affairs or the United States government.
rHealth Research and Educational Trust
Health Services Research
incentives have been associated with high rates of transitions between pro-
viders because there are no consequences for maximizing reimbursements in
this way. In particular, patients who are disabled or chronically or terminally
ill, who are often served in nursing homes (NH) as their main long-term care
provider have been subject to the consequences of the conflicting reimburse-
ment incentives and have thus suffered from multiple transitions. In spite of
the increasing recognition of the importance of care transitions among long-
term care residents, most of the literature has concentrated on reporting total
utilization per service type (mostly either inpatient or Medicare-paid skilled
nursing facility [SNF] care) (Coleman et al. 2004; Mor et al. 2010). Even the
recent focus on geographic variation in Medicare costs has emphasized re-
gional and hospital differences in the average intensity of inpatient use rather
than the extent of variation in patients’ experiences across the continuum of
care within and between geographic areas (http://www.dartmouthatlas.org).
Historically it has been difficult to assemble patient histories using ex-
isting claims data for those discharged from hospital to postacute settings
because conflicting reimbursement incentives also translate to disparate re-
imbursement systems and therefore administrative data systems. Thus, com-
posing transition histories using only Medicare claims does not provide
information on long-term NH care (Burton et al. 1995; Brown et al. 1999;
Cooper et al. 2000). Alternatively, using only NH federally mandated regular
assessment of all NH residents using the Minimum Data Set (MDS) resident
assessment instrument data provides limited information about transitions
outside of the NH (Coburn, Keith, and Bolda 2002).
The absence of data on NH use may lead to misleading conclusions.
Some studies of Medicare expenditures at the end of life found those to be
lower for older than younger persons (Gornick, McMillan, and Lubitz 1993;
Levinsky et al. 2001). However, Roos, Montgomery, and Roos (1987), using a
Canadianlongitudinaldatasetthatincluded dataonboth acuteandlong-term
care utilization, found end-of-life public health care utilization expenditures
did not decrease with age. The discrepancy between the United States and
Address correspondence to Orna Intrator, Ph.D., Associate Professor (Research), Center for
Gerontology and Health Care Research, Brown University, PO Box G-S121-6, Providence, RI
02912; e-mail: firstname.lastname@example.org. Orna Intrator, Ph.D., Health Research Scientist, VA
HSR&D REAP, Providence, RI. Jeffrey Hiris, M.A., Senior Systems Analyst, Susan C. Miller,
Ph.D., Associate Professor (Research), Vincent Mor, Ph.D., Professor, are with the Center for
Gerontology and Health Care Research, Brown University, Providence, RI. Katherine Berg,
of Toronto, Toronto, ON, Canada.
The Residential History File121
Canadian findings may be explained by the absence of NH stays in the U.S.
research and highlight the need for accurate information on all utilization
when studying expenditures and budgeting for care.
The purpose of this paper is to describe the creation of a ‘‘residential
history file’’ (RHF), using an algorithm that links Medicare claims and NH
MDS assessments that results in a dataset (the RHF) which tracks the timing
and location of health service use. Initially, we developed this method to track
postacute careforpatients hospitalizedforhipfracture orstroke(Intratoret al.
2003). Subsequently we expanded the method to other applications ranging
from studies of hospice use among Medicaid NH residents to tracking post-
hospitalization disposition of NH residents (Miller et al. 2004; Intrator et al.
to a cohort of all Medicare beneficiaries who were in one of 202 free-standing
nonpediatric NHs at some time in 2006. We describe the resulting RHF,
present an alternative RHF based only on MDS data, and conduct an illus-
trative analysis of a study of place of death. We then present results from
comparisons identifying patients in NHs based on the RHF versus on Online
Survey of Certification and Reporting (OSCAR) and on the place of service
codes recorded on Medicare part B claims.
Administrative data are a compelling source for the study of population-wide
health care utilization, patient outcomes, and organization and system eval-
researchers from the Center for Medicare and Medicaid Services (CMS). We
used Medicare claims and NH resident assessments and a facility-level
resource, the OSCAR, which reports results of the annual certification of
NHs and contains information regarding NH deficiencies as well as infor-
mation about the NH residents in aggregate. In particular, we used the re-
ported total number of NH residents and the number of Medicare
residents on the day of the survey.
MDS Data from NH Residents’ Assessments
Filing of the MDS resident assessment instrument is mandated for every NH
resident at admission, quarterly, annually, at discharge, and at any significant
change of care. The instrument contains nearly 400 data elements which have
been combined to create composite outcome scales, quality indicators, and
122 HSR: Health Services Research 46:1, Part I (February 2011)
various case mix and risk-adjustment measures (Fries et al. 1994; Hawes et al.
1995; Mor et al. 1995, 2003; Phillips and Morris 1997; Gambassi et al. 1998;
Hirdes, Frijters, and Teare 2003; Grabowski, Angelelli, and Mor 2004; Mor
et al. 2004; Wu et al. 2005). Since 1999 the MDS data have been used to
determine level of payment of Medicare billable SNF services using 44–53
Resource Utilization Groups (Fries et al. 1994). Because the MDS is used to
determine Medicare SNF payment levels, special Medicare MDS assessments
are conducted more frequently during the first several weeks of a SNF stay to
properly adjust payment levels to residents’ care needs. Since 2002 the MDS
data have been used to publically report quality of NH care (http://
www.medicare.gov/nhcompare.). The use of MDS for both payment and
quality monitoring requires that the MDS is done in a timely manner and able
to withstand audit.
Medicare Eligibility and Claims Data
Medicare’s routine administrative databases include detailed demographic,
financial, and clinical data. All records in the claims and enrollment files
include unique identifiers for Medicare enrollees that allow longitudinal link-
age, enabling detailed descriptions of the specific clinical services provided to
patients. Enrollment files include demographic data (birth date, age, gender,
race, and place of residence), eligibility information (in particular, Medicare
parts A and B eligibility and periods of HMO enrollment), and vital status
(date of death [DOD]). Medicare part A, institutional claims files, have been
used extensively in research. Generally, they contain information on dates of
claims, diagnoses, services provided, charges, and reimbursements. Outpatient
claims may be used by an NH to bill for skilled services for residents who had
exhausted their part A SNF benefits or were otherwise ineligible for SNF care.
Part B claims have been used less frequently in research. Like part A
claims, these claims contain information on the date of service, charges,
place of service. Among the 50 or so place of service codes two indicate NH
location (SNF and nursing facility). One study examined the utility of part B
claims place of service code to identify NH utilization (Iwashyna 2003). Using
self-reported utilization data from the 1993 Medicare Current Beneficiary
Survey (MCBS) for validation, it reported that the place of service code was
within a year. Although an impressive rate over a 1-year period of time, that
paper did not attempt to define the duration of an episode of NH utilization
The Residential History File123
nor to validate the accuracy of the actual place of service. We examine the
the NH (assumed as the gold standard).
The goal of the RHF is to create a per-person chronological history of health
service utilization and location of care within a prespecified calendar (e.g.,
throughout a calendar year). The first step of the algorithm assigns utilizations
and associated locations to days in a calendar. Depending upon the type of
claim, the basic information from a claim is the location of care (e.g., hospital,
NH, emergency room, and home) type of provider (e.g., free-standing, hos-
pital based, or swing bed SNF), and service type (e.g., hospice, SNF). The
order of information entered into the daily calendar structure which controls
the RHF (the data hierarchy) gives precedence to the records with dates that
are most likely to be complete and accurate because Medicare payments
depend upon them. Thus, inpatient claims are first filled into dates of the
calendarfollowed by outpatient emergency department (ER) and observation
days. Next SNF claims are entered onto days, followed by outpatient claims
filled into days. The above claims are location specific. Hospice claims are not
location specific, because hospice can be provided in community or institu-
tional settings. Thus, the location of hospice care is defined using other data
and includes hospice at home and hospice in NH. Consecutive days with the
same location and provider form episodelets of care.
Oncethecalendarispopulatedbyallinformation obtained fromclaims,
remaining nonfilled days may be populated by projected NH days based on
MDS assessment dates and type. For example, admission assessments are
required within 2 weeks of admission; therefore, the RHF fills up to 14 days
back during consecutive ‘‘gap’’ days to form an NH stay. Quarterly assess-
ments are required every 3 months; thus, any gap days within 3 months
preceding the quarterly assessment will be filled in the calendar as NH. An-
nual assessments are required each calendar year, around the time of the
closest designated quarterly assessment. Discharge tracking assessments are
required by CMS and are used to determine date of NH discharge, when
present. A full list of MDS rules is available from the authors.
The DOD is determined using information from the Medicare Denom-
inator file and claims. Death is recorded as occurring on the last RHF
episodelet, thus enabling an easy identification of place of death.
124 HSR: Health Services Research 46:1, Part I (February 2011)
episodelets may need to be linked to define an ‘‘episode of care’’ which is
relevant tothe researchquestion.Forexample,ifthetotal numberofNHdays
is of interest, several types of NH episodelets (e.g., SNF and MDS type) may
need to be joined to span the complete time in NH.
Example 1: An RHF-Based Patient History
We present an example of a history constructed for a fictional Medicare ben-
eficiary. A home health care claim was made on behalf of this individual
spanning from January 1 through January 10. However, an ER outpatient
through January 11. This was followed by a Medicare SNF claim January 11–
a Medicare specified assessment on January 27. An MDS discharge tracking
form was filed on January 31. Hospice filed a claim January 26–31, and an-
other inpatient claim was made for January 30–31. The Denominator file
reported a verified DOD on January 31.
Figure 1 is a diagram of the process of filling a calendar for the month of
January for this patient. First circles are entered to mark inpatient care (Jan-
uary 8–10, 30–31), then triangles to mark SNF care (January 11–14). Next
homehealthperiodsareentered on claimeddays(January 1–10),although we
note that they overlap ER on January 3 and inpatient claims on January 8–10.
Following home health claims, MDS assessments are entered with their type
(January 12 admission 1A,January 27 Medicare required 7O,and January 31,
discharge tracking 8D). Rules based on regulations infer NH days during gap
days (January 15–29). Hospicerecordedon January26–29 are hospicein NH,
but on January 30–31 they are added as a secondary type of episodelet to the
existing inpatient stay. Finally, death is noted on January 31st from Denom-
inator eligibility records. The table under the monthly calendar presents the
resulting RHF records.
The RHF algorithm was applied to a cohort of all Medicare beneficiaries
identified in any one of 202 free-standing NHs during 2006 collected for
anotherstudy(Katzetal.2009).AllMedicareeligibilityrecords,partsA and B
claims and MDS data, were obtained for these residents under a data use
agreement(DUA)withCMS.The RHFalgorithm wasappliedtothesedata to
create an RHF that is described and used in the following sections.
The Residential History File125
Home Health SAFHospice SAF M=MDS Assmnt
NH SNF SAF
NH (Based on MDS)Outpatient ERDeath
141516 17 1819 20
2122 23 24 2526 27
Figure 1: Sequence of Filling Calendar Days with Claims and MDS
Information for Fictional Patient
126 HSR: Health Services Research 46:1, Part I (February 2011)
or only to determine whether death occurred in the hospital. The RHF makes
it possible to locate a person when s/he dies, allowing an examination of NH
andhomeasplacesof death, and transfersin care sitesat the end of life.Those
are tabulated and the distributions described.
RHFs created using only MDS and Medicare Denominator data versus
using all part A claims, Denominator and MDS data were compared in order
to gain an understanding of the difference in identifying days in NH for res-
idents who are not Medicare beneficiaries or who are receiving Medicare
and the distribution of the difference described.
We examined the completeness of the RHF in determining NH ep-
isodelets by comparing it with two other sources. The first is the number of
residents in an NH on the day of certification reported on the OSCAR. The
second is the place of service codes on Medicare part B claims.
Because the RHF can be based on all NH residents regardless of their
in the RHF on the day of the survey should be comparable to that reported in
the OSCAR. Moreover, the OSCAR also includes a count of Medicare res-
idents which should correspond to the number of residents receiving either
SNForhospice carein anNHon the dayofthe survey.The distribution ofthe
difference between the OSCAR and RHF reports is described.
NH place of service codes in part B claim, and the episodelete type
reported inthe RHFon thedayofthe partB claim,are crosstabulated.Results
are presented with the claim/visit as the unit of analysis. Assuming that part B
place of service indicating NH is the ‘‘truth,’’ we calculated the sensitivity and
specificity of the RHF to identify days in NH. Because part B visits are not
Notes. MDSassmnt,Minimum DataSet assessment (types are 1A,admission; 7O,other Medicarerequired;8D,
discharge); NH, nursing home; SAF, Medicare claims from standard analytic files; SNF, skilled nursing facility
(Medicare paid nursing home care). Below calendar, upper panel shows the locations of care for the person in
Example 1. Location variablesare prefaced by HEE_,with variables presentedfor dates fromand through, two
overlapping locations, and indicator of death in the episodelet. Lower panel shows the data segments (claims,
The Residential History File127
Description of Study Data and Cohort
the 202 free-standing nonpediatric facilities during 2006 and 71,473 SNF
claims from 30,627 residents. A total of 61,479 residents had either SNF or
MDS assessments, of whom 28,756 (46.8 percent) had both SNF claims and
MDS assessments, another 1,774 (2.9 percent) had only SNF claims, and
30,949 (50.3 percent) residents had only MDS assessments. Of the 61,479
residents with either an MDS or an SNF claim during 2006, 132 were not
Medicare eligible reducing the sample to 61,347 residents. The RHF also
identified 363 residents who had claims after death and who were removed
from the RHF. Thus, the final RHF dataset included 60,984 individuals.
The final RHF included 506,477 episodelets lasting an average of 38.7
days (median 11 days). Among the cohort residents, 456 (0.7 percent) had no
throughout the year, and 10,446 (17.1 percent) received Medicare benefits
through a managed care organization some time throughout 2006, with 6,016
(9.9 percent) having MCO coverage throughout 2006.
The average length of stay in the hospital was 7.6 days, with a median of
5 days. The durations of SNF, MDS-based, and outpatient-based NH epi-
sodelets were 24.3 (median518), 55.6 (median57), and 13.9 (median510)
Example 2: Site of Death
Overall, 15,341 (25.2 percent) residents died during the year. Death in the
hospital occurred for 2,974 patients (19.4 percent). NH was the place of death
for 9,134 patients (59.5 percent) with 2,423 (15.8 percent) dying while receiv-
ing SNF care and 3,450 (22.5 percent) while receiving hospice care. The
the ER or during an observation stay (n5480, 3.1 percent) were assigned a
place of death based on the location they originated from. The majority 390
(2.5 percent) died after being transferred to the ER from NH and the rest (90)
died after being transferred to the ER from home.
The RHF allows us to identify the location before the site of death
(Table 1). Interestingly, 1,485 of 3,233 residents who died at home (45.1
percent) were in an NH before that. Among residents whodiedin the hospital
19.1 percent were transferred from home, 68.2 percent from an NH (36.1
128 HSR: Health Services Research 46:1, Part I (February 2011)
Table 1: Transitions from the Location before the Location of Death to Location of Death for 15,341 Cohort Decedents
Location at Time of Death, N/Row%
Location before location at time of death
Note. Location of death (columns), and location before location of death (rows).
NH, nursing home; SNF, skilled nursing facility.
The Residential History File129
percent from SNF and 32.1 percent from non-SNF), while 14.6 percent of
residents who died in the NH were transferred from home.
Example 3: Comparison of Full RHF and Resident History File Based Only on
We compared the ‘‘full RHF’’ created from all Medicare parts A claims, De-
nominator, and MDS data with an ‘‘MDS only RHF’’ created only from MDS
and Denominator data in terms of the total duration in MDS-identified NH
stay, any NH stay, and gaps, per person (Table 2). On average, the MDS-only
RHF listed more MDS days than the full-RHF (127 versus 101 MDS days,
respectively). Correspondingly, there were many more gap days in the MDS-
only RHF than in the full RHF (238 versus 181 days, respectively). However,
when comparing the total number of NH days of any type in the full RHF
versus those accounted for by MDS only, the difference was very small, on
average 1 more NH day in the full RHF versus the MDS-only RHF (inter-
quartile range [IQR] 0–2 days).
Comparison 1: RHF versus OSCAR Number of Residents on Day of Certification
Among the 202 NHs in the cohort 138 were surveyed in 2006. The OSCAR
reported an average of 130.8 (IQR 85–159) residents in the NH on the day of
the survey, and 18.6 (IQR 8–26) residents with Medicare as their primary
payer. Using the RHF we identified an average of 112.5 (IQR 65–134) res-
identsin theNH on theday ofthesurvey,and an averageof 21.0(IQR 10–29)
residents receiving Medicare paid care. On average, the RHF identified 18.3
(IQR 6–24) less residents than did OSCAR and the RHF identified 2.4 (IQR
0–6) more residents on Medicare than OSCAR.
Comparison 2: RHF NH Episodelets and Part B Claims Place of Service Code
We received a total of 5,365,457 part B claims for 53,527 beneficiaries from
our sample (91.1 percent of residents in the cohort). Of these claims 917,059
(17.1 percent) reported place of service as an NH for beneficiaries either
receiving SNF care or other non-SNF NH care. On the other hand, 1,920,728
(35.8 percent) part B claims had a date of service corresponding to when the
RHF identified the beneficiary as residing in an NH. Table 3 shows the cross-
tabulation of place of service and location of resident on the day of the part B
claim based on the RHF episodelets.
Among the 917,059 claims with place-of-service NH, 777,628 were ob-
served in an NH by the RHF. Thus, the sensitivity of the RHF in identifying
130 HSR: Health Services Research 46:1, Part I (February 2011)
Table 2: Comparison between MDS-Only and Full RHFs in Terms of Average Number of Days Per Resident
Difference: Full-MDS Only
Median (Q1, Q3)
Median (Q1, Q3)
Median (Q1, Q3)
26 (1, 239)
78 (24, 255)
?12 (?41, 0)
Any NH episodelets
78 (24, 257)
78 (24, 255)
0 (?2, 0)
162 (73, 310)
287 (110, 341)
?12 (?58, 0)
MDS, Minimum Data Set; NH, nursing home; RHF, Residential History File.
The Residential History File131
NH is 84.8 percent (777,628/917,059 part B claims). The specificity is 74.3
percent (1?[917,059–777,628]/[5,565,457–1,920,186] part B claims).
The Residential History methodology provides extremely useful information
for many health care utilization research studies. By tracking peoples’ health
making possible the examination of access barriers, and discontinuity of care.
There are many important research questions that require this technology in
at a particular day to describe their conditions (http://www.ltcfocus.org). An-
other example is identifying 30-day rehospitalizations from NH, currently
index hospitalization, and that rehospitalization only from NH be identified.
While some of these differentiations can indeed be done without the benefit of
the RHF, having it available makes creating such complex analyses files much
more efficient. Even more important, the RHF summarizes information and
knowledge about Medicare claims and MDS records that have been obtained
and accumulated by experience, and thus provides a single resource for uti-
lizing these components of data together. The RHF framework also provides a
Table3: RHF Location at Time of Part B Claims versus Part B Place of
Service (row percents)
Nursing Home HospitalHome
N Place of ServiceN%N%N%
22. Outpatient hospital
23. ER hospital
81. Independent lab
ER, emergency room; NH, nursing home; RHF, Residential History File; SNF, skilled nursing
132 HSR: Health Services Research 46:1, Part I (February 2011)
method to adjudicate overlapping claims. For example, determining DOD
requires working with information from both Denominator and claims with
knowledge-based algorithms designed to reconcile differences. Moreover, the
comparison of theRHFbased on MDS andclaims datatotheRHFbased only
on MDS and Denominator data reveals that the RHF is able to track NH
Medicare Managed Care (approximately 17 percent).
The RHF algorithm resulted in an RHF file with much face validity.
Compared with part B place of service, it was almost 85 percent sensitive in
identifying NH location. Moreover, while it is expected that episodelets in
MDS-only RHF would be longer than episodeletes in the full RHF given that
part A claims provide more transfer information, it was reassuring that the
total number of NH days identified by the two methods was similar.
Indeed, the study of site of death presented in this paper points to com-
plexities in studying site of death. Using the RHF, a more nuanced under-
standing of end-of-lifecareis gained; forexample, among decedents receiving
SNF care before their site of death, 49.5 percent were transferred to the hos-
pital to die. This compares to 18 percent of non-SNF NH decedents, raising
the question, why that great a difference?
When comparing the RHF location with an NH place of service on the
part B claims, we found the RHF correctly identified 84.8 percent of the NH
part B claims. The majority of the part B dates not identified to be in the NH
were identified in ‘‘gap’’ (presumably, community not receiving institutional
identification of NH stays using the part B claims. Indeed, if part B claims
indicating the NH as the place of service infers that the existing RHF epi-
sodelet is in an NH, we find that 94.1 percent of NH episodelets had a part B
stay with place of service NH.
The comparison of the number of residents observed on the day of the
OSCAR survey with the OSCAR report indicates that, on average, the RHF
reported 14 percent less total residents and 12.9 percent more residents re-
ceiving Medicare-paid (SNF or hospice) care than the OSCAR. However,
60 percent of facilities each year have at least 20 percent difference in total
Medicare residents. Thus, it appears that the OSCAR report may be more
erroneous than the report based on the RHF. Several other studies have
questioned the validity of the OSCAR data (OIG 2003; Feng et al. 2005).
The Residential History File 133
Others versions of the RHF reported appear to be more limited and not
well documented (Sood, Buntin, and Escarce 2008). One recent attempt to
create a file that identifies periods of NH care has been conducted internally
by CMS (L. ‘‘Spike’’ Duzor, personal communication). The ‘‘Stay File’’ iden-
tifies periods of NH stay and adjoining hospitalizations. It uses MDS data to
determine periods of NH stays by using the assessment types recorded on the
MDS and their corresponding dates. Unlike the RHF, this file was created in
an attempt to examine NH utilization in isolation of other types of utilization
and only uses the MedPAR data to determine hospitalizations surrounding or
during NH stays. Based on our experience with the RHF algorithm, it would
appearthat the useof more limited Medicare data may create a file that would
identify all NH days, but would incorrectly identify additional days as NH
days. Therefore, it is not likely that this file will be as useful in examining
transitions in care and discontinuity of care.
Several limitations are noted. The RHF methodology is usable only for
researchers who have an approved DUA with CMS to use Medicare standard
analytic files and NH MDS data. Although the information provided by the
RHF is comprehensive, it pertains mainly to the Medicare fee-for-service pop-
be about 17 percent of total expenses for Medicare eligible population (MEPS
2006). The program that implements the methodology is very computationally
intensive and quite complex spanning over 10,000 lines of SAS code.
The RHF methodology can be useful for many studies that have linked
survey data with Medicare claims and MDS data. For example, the MCBS, the
SEER cancer registry, the Health and Retirement Survey, and the currently
file of MDS and OASIS stays (F. Epig, personal communication). All these
studies can benefit from knowledge on transition sequences and NH stays.
as is on our website. However, the SAS program is very complex and would
require a lot of support at our institution, which we cannot budget. Another
method is to provide this code to CMS and to allow researchers to ask in a
DUA that an RHFbe created forthem. CMS could also incorporate theRHF,
or a reduced version of it, into the Chronic Condition Warehouse, which is
intended to serve a somewhat similar, although less dynamic purpose.
Increasingly, the U.S. health care system will need to provide care for
frail, older persons, many with a terminal disease trajectory of chronic, pro-
gressive illnesses with prolonged periods of functional dependency. Our cur-
134 HSR: Health Services Research 46:1, Part I (February 2011)
rent health care reimbursement system, for the most part, is based on fee-for-
service payment to individual institutions, with recent policy providing in-
centives for cost containment. These incentives, in part, resulted in shorter
hospital stays and a higher rate of transitions to SNFs. The population-based
design of the RHF makes it possible to conduct policy-relevant research to
examine the variation in the rate and type of health care transitions in the
United States, examine the role of state policies and market characteristics on
transition rates, and the impact on beneficiaries residing in geographic region
with differing rates and types of transitions.
Joint Acknowledgment/Disclosure Statement: Supported in part by National
Institute on Aging grants R01 AG-14427, R01 AG 020557, R21 AG 030191,
and P01 AG027296, and Agency of Healthcare Research and Quality R01
HS10549. Special thanksto JulieLima, Ph.D.,whohelped createFigure1 and
who has dealt with all the DUA issues over the past many years, to Linda
Laliberte-Cote for her confidence in this concept and in continuing to support
this effort throughout the years, and to Christian Brostrup-Jensen for his pro-
gramming support and his comments and feedback in testing the residential
history file program. A previous version of this paper was presented at the
annual meeting of the Gerontological Society of America in 2003.
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